Adequate sample sizes for improved accuracy of thermal trait estimates

Abstract

Abstract Thermal traits, such as upper and lower critical thermal limits, are vital indicators of the vulnerability of populations and species to environmental change. Thus, accurate estimates of these traits are needed to explain biological patterns and forecast responses to the changing thermal environment. However, many thermal trait studies measure relatively few individuals to estimate traits for whole populations or species. To ascertain if, and how, sample size affects the accuracy of reported trait means and variances, we applied a subsampling and equivalency testing approach to empirical and simulated trait data to investigate the accuracy of trait estimates relative to sample size and the skew and variance of the trait distribution in the source population. Simulation results indicated that only 7.9% of the 428 critical thermal limit traits documented in a recent synthesis of thermal trait data reported sufficiently large sample sizes, relative to variance, to ensure confidence in the reported mean trait value with negligible (±0.25°C) error. Greater inter‐individual trait variance in the source population requires a larger number of individuals to be measured to accurately estimate the mean and variance of that trait. This pattern is mitigated somewhat by the tendency of thermal traits to exhibit skew‐normal distributions. As measurements of few individuals from a population are unlikely to provide accurate estimates of thermal traits, the propensity towards small sample sizes in thermal trait studies is concerning. Macrophysiological syntheses often use these data to describe, explain and predict broad‐scale ecological patterns. Thus, insufficient sample sizes in the original studies could diminish the robustness of these patterns and predictions. For future studies, we recommend that preliminary data be used to estimate trait variance and calculate minimum sample sizes. If small sample sizes are unavoidable, larger error around the measured trait mean must be assumed and accounted for in subsequent analyses. A free Plain Language Summary can be found within the Supporting Information of this article.